The purpose of this study was to demonstrate the sensitivity of the AERMOD3 Model in modeling identical sources with meteorological data sets derived using both airport and industrial site land use characteristics.

1. Modeling Software for EHS Professionals
Sensitivity of AERMOD to Meteorological Data Sets
Based on Varying Surface Roughness
Paper No. 2009-A-168-AWMA
Prepared By:
Anthony J. Schroeder, CCM ▪ Senior Consultant
George J. Schewe, CCM, QEP ▪ Principal Consultant
BREEZE SOFTWARE
1717 Dixie Highway
Suite 900
Covington, KY 41011
www.trinityconsultants.c
om (859) 341-8100
June 18, 2009

2. 2
ABSTRACT
Dispersion modeling in support of regulatory programs for federal, regional, state and local
permitting relies on the U.S. Environmental Protection Agency’s AERMOD Model as specified
in the Guideline on Air Quality Models (40 CFR 51, Appendix W).1
Internal calculations in
AERMOD for daytime mixing in the convective boundary layer and night time mixing in the
stable boundary layer rely on surface land use. Three variables are derived from land use data
using EPA’s AERSURFACE2
Program, namely, albedo, Bowen ratio, and the surface roughness
parameter. The purpose of this study was to demonstrate the sensitivity of the AERMOD3
Model in modeling identical sources with meteorological data sets derived using both airport and
industrial site land use characteristics. This paper (using the results of a companion study of
land use and the derivation of these three variables with variable surface roughness) presents
combinations of meteorological data sets with representative industrial facility point and volume
sources representing stacks, transfer points, storage piles and roads. Meteorology from various
U.S. regions is used along with the land use characteristics for each airport and an urban
complex where industrial activities are likely. Flat terrain is assumed in all cases. Comparisons
between estimated concentrations for the various source types and meteorological data sets on
both an annual basis and on a short-term averaging time basis show the importance of selecting
specific land use and the possible variability that will result when following modeling guidance
from states and regions on the selection of the appropriate location to use to determine the
representative land use.
INTRODUCTION
The AERMOD Model3,4
was introduced to the regulatory dispersion modeling community in the
late 1990s. AERMOD was developed specifically by the AMS/EPA Regulatory Model
Improvement Committee (AERMIC) to employ best state-of-practice parameterizations for
characterizing the meteorological influences on dispersion. Section 4.2.2.b of the Guideline on
Air Quality Models (GAQM), Appendix W, 40 CFR Part 511
states that AERMOD is the
recommended model for “a wide range of regulatory applications in all types of terrain” thus,
officially replacing the Industrial Source Complex Model as the primary refined analytical
technique for modeling traditional stationary sources. Provided along with the AERMOD Model
are a number of preprocessors for preparing data sets applicable to running the AERMOD
algorithms for transport, dispersion, convective boundary layer turbulence, stable boundary
layer, terrain influences, building downwash, and land use. These are AERMAP,
AERSURFACE, and AERMET. AERMAP is used to process elevation data from digitized data
sets to generate elevations of receptors, sources, and structures as well the critical height for each
receptor. AERSURFACE uses land use land cover (LULC) data to calculate albedo, Bowen
ratio, and the surface roughness parameter which can vary on an annual, seasonal, or monthly
basis for one or up to twelve sectors around a site. AERMET is the meteorological data
processor that uses a combination of surface observation data from the National Weather Service
(NWS), upper air data from NWS, onsite data if available and meeting prescribed collection and
quality assurance criteria, and albedo, Bowen ratio, and surface roughness parameters from
AERSURFACE.

3. 3
Current guidance on the use of AERSURFACE for deriving the three meteorological variables is
to apply the program for the LULC data set at the location of the weather data collection. This is
generally the location of the weather instruments at the data collection site. As seen in the
companion paper to this paper, namely, Sensitivity of AERSURFACE Results to Study Area and
Location,5
albedo and Bowen ratio vary but do not have a significant impact on air
concentrations. Surface roughness values, however, which do vary widely 1) when comparing
sites at different locations on the airport, 2) when using the AERSURFACE recommended 1 km
radius to determine surface roughness versus the 3 km radius recommended in previous EPA
modeling guidance, and 3) when comparing airport sites to industrial facility sites (where the
meteorological data would be deemed representative considering geographical setting and
general meteorological concerns), have been shown to have significant impact on modeled
concentrations.
The Environmental Protection Agency (EPA) has recognized this situation in recent meetings.
At the June 11, 2008 meeting of the EPA Regional/State/Local Modelers Workshop, the
AERMOD Implementation Workgroup (AIWG), Surface Characteristics Subgroup presented
their findings6
concerning three surface characteristics issues when comparing a meteorological
collection site to an application site for a source: 1) lack of representative meteorological data, 2)
parameter determination (albedo, Bowen ratio, and surface roughness), and 3) representativeness
of using meteorological data from an airport to an application (industrial) site with dissimilar
land use. Graphical findings were presented with indicated differences over many source types
ranging from higher concentrations to lower concentrations when comparing the concentration
ratios at the airport with a 1 km radius and a 3 km radius for the surface roughness calculations.
The current analysis expands this work by looking at both the 1 km and 3 km radius for surface
roughness calculation at the airport site but considering differences in the National Weather
Service (NWS) coordinates (from the National Data Climatic Center, NCDC) and estimated
locations of the weather data collection. This paper also considers an alternative non-airport site
such as at a potential industrial site also at both a 1 km and 3 km radius. The derived surface
characteristics are used in the AERMET processor with meteorological data and thereafter to
perform dispersion modeling using AERMOD. The results of these analyses were used to
discern the sensitivity of air concentration estimates for three NWS sites, three source types, and
three averaging periods (including short term and long term) using surface characteristics for a
three possible locations near each airport (airport and non-airport) and two radii distances.
METHODOLOGY
The basic methodology conducted in this analysis followed the recommendations of the GAQM1
for application of the AERMOD Model. These recommendations included the use of regulatory
options, the characterization of sources appropriately, hourly meteorological data based on
nearby NWS data and processed in the AERMET program, surface characteristics based on
AERSURFACE processing of NLCD92, local land use data (used in AERMET), and the
tabulation of concentrations over 3-hr, 24-hr, and annual average time periods. To minimize the

4. 4
effects of other influencing modeling features, terrain was assumed to be flat in all cases and no
building downwash was considered. Notable differences in this analysis to that of EPA6
were
the use of fewer source types and the use of a nested 100m and 250m Cartesian receptor grid
covering a domain of 10km by 10km. Also to minimize the effects of high impacts due to the
surface-based volume source in the extreme near field, a square fence line at a distance of 100m
was positioned around all sources.
Locations and Land Use
Study locations were defined in three general areas of the United States, the Eastern U.S., the
Central U.S., and the Western U.S. Three airports were chosen as representative of these three
general locations, namely, the Albany Airport (ALB, NWS No. 14735) located in Albany, New
York, the Jackson Julian Carroll Airport (JKL, NWS No. 03889) located near Jackson,
Kentucky, and the Pocatello Regional Airport (PIH, NWS No. 24156) located in Pocatello,
Idaho. To represent surface characteristics of typical industrial operations in each of these
regions, an industrial facility located in the vicinity of each airport was chosen for inclusion in
this study.
The area surrounding the Albany airport consisted of primarily both high and low intensity
residential and commercial/industrial/transportation land use with smaller areas of deciduous and
evergreen forest, pasture/hay, and small grains. The area surrounding the nearby selected plant
site consisted primarily of industrial quarries, haul roads and industrial operations along with
mixed forest land use categories and smaller areas of pasture/hay,
commercial/industrial/transportation, and evergreen forest land use.
The area surrounding the Jackson airport was rather homogeneous and primarily contained
deciduous forest. Some smaller areas of pasture/hay, mixed forest, and evergreen forest land use
types are also located near Jackson airport. The nearby selected plant site location was
surrounded by a much more complex mixture of land use types compared with the airport site.
Primary land use types were transitional barren to the northeast, deciduous forest and open water
to the southeast, pasture/hay and deciduous forest to the southwest, and deciduous forest
pasture/hay, and open water to the northwest.
The area surrounding the Pocatello airport consisted generally of shrubland in all directions, with
some row crops to the southeast, commercial/industrial/transportation to the southwest (airport
buildings), and orchards/vineyards/other to the northwest. The nearby selected plant site
location consisted of a patchwork of row crops, orchards/vineyards/other, shrubland,
pasture/hay, and an actual facility which included commercial/industrial/transportation land use
within the study area.
The AERSURFACE2
tool was used to process the land use in the vicinity of each airport. Five
AERSURFACE applications were processed using a 1 km and 3 km radius at the NCDC-
specified tower location, a 1 km radius at an alternative tower location at the airport, and a 1 km
and 3 km radius at a pseudo industrial location. The results of this application of
AERSURFACE are reported in the report Sensitivity of AERSURFACE Results to Study Area
and Location.4
Two of the sites, namely, Albany and Pocatello, have greater surface roughness

5. 5
elements for the 3 km radius compared to the 1 km radius namely, 15 and 20 percent, while the
third site in Jackson with relatively consistent deciduous forest land use has only about a 1
percent increase. For Albany the selected pseudo plant site had higher roughness that the airport
while for Jackson and Pocatello, the pseudo plant sites had lower surface roughness (cleared land
versus forested airport site at Jackson and open range versus buildings at the airport). Thus, the
determinations of surface roughness gave a good cross section of variable land use on and off
airports.
Meteorological Processing
The AERMET program was used along with the AERSURFACE results for albedo, Bowen
ratio, and surface roughness parameter to generate 15 sets of meteorological data. A 1992 data
set of SCRAM formatted surface data for Albany, Jackson, and Pocatello and fixed format TD-
6201 upper air profiles for Albany, Huntington, and Boise were used for each set of surface
roughness parameters. Figures 1 through 3 show wind roses for each site for the NCDC tower
location and a 1 km radius. Wind roses for these 1 km radius meteorological data sets showed
little variation to those at alternative airport or industrial locations or at a 3 km radius for surface
roughness. Wind speed and wind direction are little affected by the surface roughness parameter
and convective and stable boundary layer parameters. Each site is characterized by multiple
dominant wind directions and speeds and are mildly affected by local geographical features
(Albany by the Hudson and Mohawk River Valleys, Jackson by the surrounding forested land,
and Pocatello by the Snake River Valley).
Sources
Three sources were modeled in this analysis representing a tall buoyant stack, a shorter less
buoyant stack, and a small storage pile represented by a volume source. All sources were
assumed to be collocated at the center of a coordinate system located at the NCDC coordinates,
he alternate airport location, or at the pseudo plant site. The influences of building or structure
downwash and wakes were not included in the analysis. Parameters defining the physical
characteristics of each source are shown in Table 1.
Figure 1. Wind Rose for Albany, NY. Figure 2. Wind Rose for Jackson, KY.

6. 6
Figure 3. Wind Rose for Pocatello, ID.

7. 7
Table 1. Source Characteristics Used in Modeling Sensitivity Analysis.
Point
Source
ID
Stack height
(m)
Stack gas
exit temp.
(K)
Stack gas
exit velocity
(m/s)
Stack gas
diameter
(m)
Emission
rate (g/s)
STACK65 65 425. 15 5.0 500.0
STACK35 35 432. 11.7 2.4 50.0
Volume
Source
ID
Release
height
(m)
Physical
height (m)
Horizontal
dimensions
(m)
Emission
rate (g/s)
Volume 5 10 20X20 5.0
Receptors
An array of receptors were placed around each airport and plant site. A fence line was assumed
around each set of sources at a 100m distance in a square. A fence line receptor grid was
arranged at a 50m spacing, a 100m grid out to 2km around each site, and a 250m grid out to
5km. A total of about 3,000 receptors were used in the modeling.
Model Scenarios and Analysis
Each set of source parameters were modeled using AERMOD (Version 07026) along with each
set of meteorological data. Concentrations were calculated for a 3hr, 24hr, and annual averaging
period. The concentration associated with the meteorological data set using the NCDC 1 km
radius surface roughness parameters was considered as the baseline for each site. This baseline
was selected because this scenario followed the AERSURFACE application guidance.
Concentration differences between each scenario and the baseline were then tabulated.
RESULTS
Tables 2 through 10 present the comparisons between air concentrations derived on the basis of
the baseline location meteorological data and a 1 km radius with concentrations derived for each
meteorological scenario for considering surface roughness. All source types are described in
each set of tables. The tables are arranged in sets of three by meteorological data location,
Tables 2 through 4 are for Albany, Tables 5 through 7 are for Jackson, and Table 8 through 10
are for Pocatello. Within each table, which applies to one source type, are found five scenarios
of surface roughness/meteorology. The only difference between meteorological data sets is the
surface roughness parameters applicable to each scenario. All other AERMET processing
related operations are identical. Concentrations are estimated using AERMOD with each source
for each meteorological scenario.

8. 8
Tables 2 through 4 show the comparison of concentrations for the Albany airport sites and a
nearby pseudo industrial site. The surface roughness at this airport includes grassy areas and
buildings at the airport or just beyond the airport property. Thus, as the radius at the airport is
increased, the surface roughness is greater and the associated turbulence in AERMOD increases.
The increased turbulence causes more mixing of all source plumes and is seen as increased
concentrations for the tall point sources (with some contribution from increased scaling of
turbulence and wind speed with height as well) and decreased concentrations for the lower
emitting volume source. As surface roughness increases to its greatest value at the 3 km radius
at the pseudo plant site, point source concentrations are at their highest concentrations when
Table 2. Ratio of Scenario to NCDC 1 km Concentrations at Albany – 35m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 35m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Albany Alternative Tower 0.90 0.93 0.93 0.94 0.91
Albany NCDC 1km 1.00 1.00 1.00 1.00 1.00
Albany NCDC 3km 1.01 1.04 1.08 1.04 1.00
Albany Plant 1km 1.05 1.07 1.13 1.12 1.10
Albany Plant 3km 1.07 1.15 1.30 1.24 1.30
Table 3. Ratio of Scenario to NCDC 1 km Concentrations at Albany – 65m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 65m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Albany Alternative Tower 0.87 0.83 0.77 0.75 0.88
Albany NCDC 1km 1.00 1.00 1.00 1.00 1.00
Albany NCDC 3km 0.91 0.89 0.94 0.95 1.01
Albany Plant 1km 1.00 0.97 1.04 1.01 1.09
Albany Plant 3km 1.09 1.07 1.26 1.19 1.32
Table 4. Ratio of Scenario to NCDC 1 km Concentrations at Albany – Volume.
Ratio of Scenario to NCDC 1 km Concentrations ‐ Volume
Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Albany Alternative Tower 0.76 0.76 0.92 0.81 1.12
Albany NCDC 1km 1.00 1.00 1.00 1.00 1.00
Albany NCDC 3km 0.81 0.77 0.85 0.86 0.94
Albany Plant 1km 0.67 0.68 0.85 0.72 0.94
Albany Plant 3km 0.79 0.75 0.77 0.72 0.75

9. 9
compared to the baseline (more turbulence with better mixing to ground) and the volume source
at its lowest concentrations (also better mixing but at ground level which reduces ground level
concentrations).
Tables 5 through 7 show the comparison of concentrations for the Jackson airport and nearby
area. The surface roughness at this airport includes forested areas and buildings at the airport or
just beyond the airport property. Thus, as the radius at the airport is increased, the surface
roughness stays about the same and the associated turbulence in AERMOD also stays about the
same. This causes little change in the turbulence and mixing and thus, concentrations are not
significantly affected. At the pseudo industrial location, surface roughness is decreased at
Table 5. Ratio of Scenario to NCDC 1 km Concentrations at Jackson – 35m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 35m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Jackson Alternative Tower 1.00 1.00 0.99 1.00 0.99
Jackson NCDC 1km 1.00 1.00 1.00 1.00 1.00
Jackson NCDC 3km 1.00 1.00 0.99 1.00 1.00
Jackson Plant 1km 0.69 0.69 0.43 0.46 0.47
Jackson Plant 3km 0.80 0.76 0.63 0.65 0.58
Table 6. Ratio of Scenario to NCDC 1 km Concentrations at Jackson – 65m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 65m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Jackson Alternative Tower 1.02 1.00 0.99 1.00 0.99
Jackson NCDC 1km 1.00 1.00 1.00 1.00 1.00
Jackson NCDC 3km 1.02 1.00 1.00 1.00 1.00
Jackson Plant 1km 0.79 0.73 0.45 0.47 0.54
Jackson Plant 3km 0.79 0.73 0.54 0.57 0.64
Table 7. Ratio of Scenario to NCDC 1 km Concentrations at Jackson – Volume.
Ratio of Scenario to NCDC 1 km Concentrations ‐ Volume
Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Jackson Alternative Tower 0.97 0.92 0.94 0.95 0.96
Jackson NCDC 1km 1.00 1.00 1.00 1.00 1.00
Jackson NCDC 3km 0.97 0.92 0.94 0.94 0.97
Jackson Plant 1km 3.28 3.17 2.77 3.13 1.81
Jackson Plant 3km 2.62 2.92 2.28 2.48 1.63

10. 10
Jackson in the suburban and rural areas and thus turbulence and mixing are decreased. Point
source concentrations are lower at this location than the base site and volume source
concentrations are increased (poorer mixing but at ground level which increases ground level
concentrations).
Tables 8 through 10 show the comparison of concentrations for the Pocatello airport and nearby
area. The surface roughness at this airport includes grassy areas and a few scattered buildings at
the airport. Just beyond the airport property lies open range, farming, scrub areas. Thus, as the
Table 8. Ratio of Scenario to NCDC 1 km Concentrations at Pocatello – 35m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 35m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Pocatello Alternative
Tower
0.96 1.00 0.80 0.85 0.95
Pocatello NCDC 1km 1.00 1.00 1.00 1.00 1.00
Pocatello NCDC 3km 0.96 1.00 0.91 0.98 0.98
Pocatello Plant 1km 0.99 1.00 0.81 0.89 1.00
Pocatello Plant 3km 0.99 1.01 0.81 0.89 0.98
Table 9. Ratio of Scenario to NCDC 1 km Concentrations at Pocatello – 65m Stack.
Ratio of Scenario to NCDC 1 km Concentrations ‐ 65m
Stack Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Pocatello Alternative
Tower
0.95 0.94 0.99 1.00 0.97
Pocatello NCDC 1km 1.00 1.00 1.00 1.00 1.00
Pocatello NCDC 3km 0.95 0.98 1.00 1.00 1.00
Pocatello Plant 1km 0.98 0.96 1.00 1.02 1.01
Pocatello Plant 3km 0.99 0.97 0.99 1.01 0.99
Table 10. Ratio of Scenario to NCDC 1 km Concentrations at Pocatello – Volume.
Ratio of Scenario to NCDC 1 km Concentrations ‐ Volume
Scenario
3hr HH 3hr HSH 24hr HH 24hr HSH Annual
Pocatello Alternative
Tower
1.06 1.15 1.10 1.22 1.16
Pocatello NCDC 1km 1.00 1.00 1.00 1.00 1.00
Pocatello NCDC 3km 1.07 1.07 1.09 1.04 1.09
Pocatello Plant 1km 1.13 1.18 1.11 1.10 1.19
Pocatello Plant 3km 1.11 1.21 1.11 1.23 1.19

11. 11
radius at the airport is increased, the surface roughness slightly decreases with subsequent
decreased turbulence. This causes less mixing of all sources which causes slightly decreased
concentrations for the point sources and increased concentrations for the lower volume source.
The off airport pseudo industrial location has even less roughness over the area and thus,
turbulence is diminished from the base case and concentrations increase.
These tables indicated that the magnitude of air concentration differences may be potentially
significant depending on both the airport location, its situation within specific land use types, the
type of source and the consideration of either the airport or the industrial location. As an
additional indication of the comparison of the air concentrations of pollutant at various averaging
times for different locations or radius of influence, Figures 4-9 present comparisons. Figures 4
through 6 present all alternative scenario-based concentrations compared to the base case for all
airport sites combined for all stacks combined. The annual comparisons in Figure 4 show
concentrations that are nearly equal between the scenario impacts and the base case. A few
outliers for the plant 1 km and 3 km radii indicate a significant change in surface roughness
which yielded some concentrations lower for the scenario (Jackson) and a few higher (Albany).
In Figures 5 and 6 a similar pattern emerges and the grouping of the concentration comparisons
shows the differences in the airport locations.
Figure 4. Comparison of All Scenario Concentrations Versus the Base Case for Annual
Average and All Stacks.

12. 12
Figure 5. Comparison of All Scenario Concentrations Versus the Base Case for 24hr
Average and All Stacks.
Figure 6. Comparison of All Scenario Concentrations Versus the Base Case for 3hr
Average and All Stacks.

13. 13
For volume sources, Figures 7-9 show that for areas with less surface roughness, volume sources
are estimated to have higher concentrations than the base case and conversely for areas with
more surface roughness, the concentrations due to volume sources are less than the base case. A
similar pattern occurs across all averaging periods.
Figure 7. Comparison of All Scenario Concentrations Versus the Base Case for Annual
Average and All Volume Sources.
Figure 8. Comparison of All Scenario Concentrations Versus the Base Case for 24hr
Average and All Volume Sources.

14. 14
Figure 9. Comparison of All Scenario Concentrations Versus the Base Case for 3hr
Average and All Volume Sources.
SUMMARY
The selection of the appropriate radius around a meteorological data site for the determination of
surface roughness makes a significant difference in the magnitude of the related air
concentrations. The AERSURFACE guidance as well as the latest AERMOD implementation
guidance suggests the use of a 1 km radius for this determination. The guidance also indicates
that the location of the data collection site at the airport should be the center of the radius. The
use of the 1 km radius when compared to a 3 km radius has been shown to yield different results
in terms of the surface roughness elements as well as the associated air concentrations. The
identification of the correct location of the meteorological tower was also shown to affect the
surface roughness and air concentrations. Finally, the selection of land use at the airport versus a
potential industrial facility site shows that the differences can give different air concentrration
results depending on the source type.

15. 15
REFERENCES
1. Guideline on Air Quality Models. Appendix W to 40 CFR Parts 51 and 52. Federal
Register, November 9, 2005. pp. 68217-68261. 2005.
2. AERSURFACE Users Guide. U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina. January 2008.
3. AERMOD Implementation Guide. U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina. Revised January 2008.
4. User’s Guide for the AMS/EPA Regulatory Model - AERMOD. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina. Revised September 2004.
5. Schroeder, Tony and G. Schewe, Sensitivity of AERSURFACE Results to Study Area and
Location, presented at the 102nd Air & Waste Management Association Conference, Detroit,
Michigan, June 15-19, 2009.
6. Surface Characteristics. Presentation by the AERMOD Implementation Work Group,
Surface Characteristics Subgroup, Environmental Protection Agency
Regional/State/Local Modelers Workshop, Denver, Colorado, June 11, 2008.
KEYWORDS
AERMOD, AERSURFACE, Dispersion, Meteorology